七种常见阈值分割代码(Otsu、最大熵、迭代法、自适应阀值、手动、迭代法、基本全局阈值法)
一、工具：VC+OpenCV
二、语言：C++
三、原理(略)
四、程序
主程序(核心部分)
代码
/*===============================图像分割=====================================*/
/*---------------------------------------------------------------------------*/
/*手动设置阀值*/
IplImage* binaryImg = cvCreateImage(cvSize(w, h),IPL_DEPTH_8U, 1);
cvThreshold(smoothImgGauss,binaryImg,71,255,CV_THRESH_BINARY);
cvNamedWindow("cvThreshold", CV_WINDOW_AUTOSIZE );
cvShowImage( "cvThreshold", binaryImg );
//cvReleaseImage(&binaryImg);
/*---------------------------------------------------------------------------*/
/*自适应阀值 //计算像域邻域的平均灰度，来决定二值化的值*/
IplImage* adThresImg = cvCreateImage(cvSize(w, h),IPL_DEPTH_8U, 1);
double max_value=255;
int adpative_method=CV_ADAPTIVE_THRESH_GAUSSIAN_C;//CV_ADAPTIVE_THRESH_MEAN_C
int threshold_type=CV_THRESH_BINARY;
int block_size=3;//阈值的象素邻域大小
int offset=5;//窗口尺寸
cvAdaptiveThreshold(smoothImgGauss,adThresImg,max_value,adpative_method,threshold_type,block_size,offset);
cvNamedWindow("cvAdaptiveThreshold", CV_WINDOW_AUTOSIZE );
cvShowImage( "cvAdaptiveThreshold", adThresImg );
cvReleaseImage(&adThresImg);
/*---------------------------------------------------------------------------*/
/*最大熵阀值分割法*/
IplImage* imgMaxEntropy = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
MaxEntropy(smoothImgGauss,imgMaxEntropy);
cvNamedWindow("MaxEntroyThreshold", CV_WINDOW_AUTOSIZE );
cvShowImage( "MaxEntroyThreshold", imgMaxEntropy );//显示图像
cvReleaseImage(&imgMaxEntropy );
/*---------------------------------------------------------------------------*/
/*基本全局阀值法*/
IplImage* imgBasicGlobalThreshold = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
cvCopyImage(srcImgGrey,imgBasicGlobalThreshold);
int pg[256],i,thre;
for (i=0;i<256;i++) pg[i]=0;
for (i=0;i<imgBasicGlobalThreshold->imageSize;i++) // 直方图统计
pg[(BYTE)imgBasicGlobalThreshold->imageData[i]]++;
thre = BasicGlobalThreshold(pg,0,256); // 确定阈值
cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<<thre<<endl;//输出显示阀值
cvThreshold(imgBasicGlobalThreshold,imgBasicGlobalThreshold,thre,255,CV_THRESH_BINARY); // 二值化
cvNamedWindow("BasicGlobalThreshold", CV_WINDOW_AUTOSIZE );
cvShowImage( "BasicGlobalThreshold", imgBasicGlobalThreshold);//显示图像
cvReleaseImage(&imgBasicGlobalThreshold);
/*---------------------------------------------------------------------------*/
/*OTSU*/
IplImage* imgOtsu = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
cvCopyImage(srcImgGrey,imgOtsu);
int thre2;
thre2 = otsu2(imgOtsu);
cout<<"The Threshold of this Image in Otsu is:"<<thre2<<endl;//输出显示阀值
cvThreshold(imgOtsu,imgOtsu,thre2,255,CV_THRESH_BINARY); // 二值化
cvNamedWindow("imgOtsu", CV_WINDOW_AUTOSIZE );
cvShowImage( "imgOtsu", imgOtsu);//显示图像
cvReleaseImage(&imgOtsu);
/*---------------------------------------------------------------------------*/
/*上下阀值法：利用正态分布求可信区间*/
IplImage* imgTopDown = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
cvCopyImage(srcImgGrey,imgTopDown);
CvScalar mean ,std_dev;//平均值、 标准差
double u_threshold,d_threshold;
cvAvgSdv(imgTopDown,&mean,&std_dev,NULL);
u_threshold = mean.val[0] +2.5* std_dev.val[0];//上阀值
d_threshold = mean.val[0] -2.5* std_dev.val[0];//下阀值
//u_threshold = mean + 2.5 * std_dev; //错误
//d_threshold = mean - 2.5 * std_dev;
cout<<"The TopThreshold of this Image in TopDown is:"<<d_threshold<<endl;//输出显示阀值
cout<<"The DownThreshold of this Image in TopDown is:"<<u_threshold<<endl;
cvThreshold(imgTopDown,imgTopDown,d_threshold,u_threshold,CV_THRESH_BINARY_INV);//上下阀值
cvNamedWindow("imgTopDown", CV_WINDOW_AUTOSIZE );
cvShowImage( "imgTopDown", imgTopDown);//显示图像
cvReleaseImage(&imgTopDown);
/*---------------------------------------------------------------------------*/
/*迭代法*/
IplImage* imgIteration = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
cvCopyImage(srcImgGrey,imgIteration);
int thre3,nDiffRec;
thre3 =DetectThreshold(imgIteration, 100, nDiffRec);
cout<<"The Threshold of this Image in imgIteration is:"<<thre3<<endl;//输出显示阀值
cvThreshold(imgIteration,imgIteration,thre3,255,CV_THRESH_BINARY_INV);//上下阀值
cvNamedWindow("imgIteration", CV_WINDOW_AUTOSIZE );
cvShowImage( "imgIteration", imgIteration);
cvReleaseImage(&imgIteration);
模块程序
迭代法
代码
/*======================================================================*/
/* 迭代法*/
/*======================================================================*/
// nMaxIter：最大迭代次数；nDiffRec：使用给定阀值确定的亮区与暗区平均灰度差异值
int DetectThreshold(IplImage*img, int nMaxIter, int& iDiffRec) //阀值分割：迭代法
{
//图像信息
int height = img->height;
int width = img->width;
int step = img->widthStep/sizeof(uchar);
uchar *data = (uchar*)img->imageData;
iDiffRec =0;
int F[256]={ 0 }; //直方图数组
int iTotalGray=0;//灰度值和
int iTotalPixel =0;//像素数和
byte bt;//某点的像素值
uchar iThrehold,iNewThrehold;//阀值、新阀值
uchar iMaxGrayValue=0,iMinGrayValue=255;//原图像中的最大灰度值和最小灰度值
uchar iMeanGrayValue1,iMeanGrayValue2;
//获取(i,j)的值，存于直方图数组F
for(int i=0;i<width;i++)
{
for(int j=0;j<height;j++)
{
bt = data[i*step+j];
if(bt<iMinGrayValue)
iMinGrayValue = bt;
if(bt>iMaxGrayValue)
iMaxGrayValue = bt;
F[bt]++;
}
}
iThrehold =0;//
iNewThrehold = (iMinGrayValue+iMaxGrayValue)/2;//初始阀值
iDiffRec = iMaxGrayValue - iMinGrayValue;
for(int a=0;(abs(iThrehold-iNewThrehold)>0.5)&&a<nMaxIter;a++)//迭代中止条件
{
iThrehold = iNewThrehold;
//小于当前阀值部分的平均灰度值
for(int i=iMinGrayValue;i<iThrehold;i++)
{
iTotalGray += F[i]*i;//F[]存储图像信息
iTotalPixel += F[i];
}
iMeanGrayValue1 = (uchar)(iTotalGray/iTotalPixel);
//大于当前阀值部分的平均灰度值
iTotalPixel =0;
iTotalGray =0;
for(int j=iThrehold+1;j<iMaxGrayValue;j++)
{
iTotalGray += F[j]*j;//F[]存储图像信息
iTotalPixel += F[j];
}
iMeanGrayValue2 = (uchar)(iTotalGray/iTotalPixel);
iNewThrehold = (iMeanGrayValue2+iMeanGrayValue1)/2; //新阀值
iDiffRec = abs(iMeanGrayValue2 - iMeanGrayValue1);
}
//cout<<"The Threshold of this Image in imgIteration is:"<<iThrehold<<endl;
return iThrehold;
}
Otsu代码一
代码
/*======================================================================*/
/* OTSU global thresholding routine */
/* takes a 2D unsigned char array pointer, number of rows, and */
/* number of cols in the array. returns the value of the threshold */
/*parameter:
*image --- buffer for image
rows, cols --- size of image
x0, y0, dx, dy --- region of vector used for computing threshold
vvv --- debug option, is 0, no debug information outputed
*/
/*
OTSU 算法可以说是自适应计算单阈值（用来转换灰度图像为二值图像）的简单高效方法。
下面的代码最早由 Ryan Dibble提供，此后经过多人Joerg.Schulenburg, R.Z.Liu 等修改，补正。
算法对输入的灰度图像的直方图进行分析，将直方图分成两个部分，使得两部分之间的距离最大。
划分点就是求得的阈值。
*/
/*======================================================================*/
int otsu (unsigned char*image, int rows, int cols, int x0, int y0, int dx, int dy, int vvv)
{
unsigned char*np; // 图像指针
int thresholdValue=1; // 阈值
int ihist[256]; // 图像直方图，256个点
int i, j, k; // various counters
int n, n1, n2, gmin, gmax;
double m1, m2, sum, csum, fmax, sb;
// 对直方图置零
memset(ihist, 0, sizeof(ihist));
gmin=255; gmax=0;
// 生成直方图
for (i = y0 +1; i < y0 + dy -1; i++)
{
np = (unsigned char*)image[i*cols+x0+1];
for (j = x0 +1; j < x0 + dx -1; j++)
{
ihist[*np]++;
if(*np > gmax) gmax=*np;
if(*np < gmin) gmin=*np;
np++; /* next pixel */
}
}
// set up everything
sum = csum =0.0;
n =0;
for (k =0; k <=255; k++)
{
sum += (double) k * (double) ihist[k]; /* x*f(x) 质量矩*/
n += ihist[k]; /* f(x) 质量 */
}
if (!n)
{
// if n has no value, there is problems...
fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
return (160);
}
// do the otsu global thresholding method
fmax =-1.0;
n1 =0;
for (k =0; k <255; k++)
{
n1 += ihist[k];
if (!n1)
{
continue;
}
n2 = n - n1;
if (n2 ==0)
{
break;
}
csum += (double) k *ihist[k];
m1 = csum / n1;
m2 = (sum - csum) / n2;
sb = (double) n1 *(double) n2 *(m1 - m2) * (m1 - m2);
/* bbg: note: can be optimized. */
if (sb > fmax)
{
fmax = sb;
thresholdValue = k;
}
}
// at this point we have our thresholding value
// debug code to display thresholding values
if ( vvv &1 )
fprintf(stderr,"# OTSU: thresholdValue = %d gmin=%d gmax=%d\n",
thresholdValue, gmin, gmax);
return(thresholdValue);
}
Otsu代码二
代码
/*======================================================================*/
/* OTSU global thresholding routine */
/*======================================================================*/
int otsu2 (IplImage *image)
{
int w = image->width;
int h = image->height;
unsigned char*np; // 图像指针
unsigned char pixel;
int thresholdValue=1; // 阈值
int ihist[256]; // 图像直方图，256个点
int i, j, k; // various counters
int n, n1, n2, gmin, gmax;
double m1, m2, sum, csum, fmax, sb;
// 对直方图置零...
memset(ihist, 0, sizeof(ihist));
gmin=255; gmax=0;
// 生成直方图
for (i =0; i < h; i++)
{
np = (unsigned char*)(image->imageData + image->widthStep*i);
for (j =0; j < w; j++)
{
pixel = np[j];
ihist[ pixel]++;
if(pixel > gmax) gmax= pixel;
if(pixel < gmin) gmin= pixel;
}
}
// set up everything
sum = csum =0.0;
n =0;
for (k =0; k <=255; k++)
{
sum += k * ihist[k]; /* x*f(x) 质量矩*/
n += ihist[k]; /* f(x) 质量 */
}
if (!n)
{
// if n has no value, there is problems...
//fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
thresholdValue =160;
goto L;
}
// do the otsu global thresholding method
fmax =-1.0;
n1 =0;
for (k =0; k <255; k++)
{
n1 += ihist[k];
if (!n1) { continue; }
n2 = n - n1;
if (n2 ==0) { break; }
csum += k *ihist[k];
m1 = csum / n1;
m2 = (sum - csum) / n2;
sb = n1 * n2 *(m1 - m2) * (m1 - m2);
/* bbg: note: can be optimized. */
if (sb > fmax)
{
fmax = sb;
thresholdValue = k;
}
}
L:
for (i =0; i < h; i++)
{
np = (unsigned char*)(image->imageData + image->widthStep*i);
for (j =0; j < w; j++)
{
if(np[j] >= thresholdValue)
np[j] =255;
else np[j] =0;
}
}
//cout<<"The Threshold of this Image in Otsu is:"<<thresholdValue<<endl;
return(thresholdValue);
}
最大熵阀值
代码 /*============================================================================ = 代码内容：最大熵阈值分割 = 修改日期:2009-3-3 = 作者:crond123 = 博客:http://blog.csdn.net/crond123/ = E_Mail:[email protected] ===============================================================================*/ // 计算当前位置的能量熵 double caculateCurrentEntropy(CvHistogram * Histogram1,int cur_threshold,entropy_state state) { int start,end; int total =0; double cur_entropy =0.0; if(state == back) { start =0; end = cur_threshold; } else { start = cur_threshold; end =256; } for(int i=start;i<end;i++) { total += (int)cvQueryHistValue_1D(Histogram1,i);//查询直方块的值 P304 } for(int j=start;j<end;j++) { if((int)cvQueryHistValue_1D(Histogram1,j)==0) continue; double percentage = cvQueryHistValue_1D(Histogram1,j)/total; /*熵的定义公式*/ cur_entropy +=-percentage*logf(percentage); /*根据泰勒展式去掉高次项得到的熵的近似计算公式 cur_entropy += percentage*percentage;*/ } return cur_entropy; // return (1-cur_entropy); } //寻找最大熵阈值并分割 void MaxEntropy(IplImage *src,IplImage *dst) { assert(src != NULL); assert(src->depth ==8&& dst->depth ==8); assert(src->nChannels ==1); CvHistogram * hist = cvCreateHist(1,&HistogramBins,CV_HIST_ARRAY,HistogramRange);//创建一个指定尺寸的直方图 //参数含义：直方图包含的维数、直方图维数尺寸的数组、直方图的表示格式、方块范围数组、归一化标志 cvCalcHist(&src,hist);//计算直方图 double maxentropy =-1.0; int max_index =-1; // 循环测试每个分割点，寻找到最大的阈值分割点 for(int i=0;i<HistogramBins;i++) { double cur_entropy = caculateCurrentEntropy(hist,i,object)+caculateCurrentEntropy(hist,i,back); if(cur_entropy>maxentropy) { maxentropy = cur_entropy; max_index = i; } } cout<<"The Threshold of this Image in MaxEntropy is:"<<max_index<<endl; cvThreshold(src, dst, (double)max_index,255, CV_THRESH_BINARY); cvReleaseHist(&hist); }
基本全局阀值法
代码
/*============================================================================
= 代码内容：基本全局阈值法
==============================================================================*/
int BasicGlobalThreshold(int*pg,int start,int end)
{ // 基本全局阈值法
int i,t,t1,t2,k1,k2;
double u,u1,u2;
t=0;
u=0;
for (i=start;i<end;i++)
{
t+=pg[i];
u+=i*pg[i];
}
k2=(int) (u/t); // 计算此范围灰度的平均值
do
{
k1=k2;
t1=0;
u1=0;
for (i=start;i<=k1;i++)
{ // 计算低灰度组的累加和
t1+=pg[i];
u1+=i*pg[i];
}
t2=t-t1;
u2=u-u1;
if (t1)
u1=u1/t1; // 计算低灰度组的平均值
else
u1=0;
if (t2)
u2=u2/t2; // 计算高灰度组的平均值
else
u2=0;
k2=(int) ((u1+u2)/2); // 得到新的阈值估计值
}
while(k1!=k2); // 数据未稳定，继续
//cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<<k1<<endl;
return(k1); // 返回阈值
}